Struggling with IP Geocoding Accuracy for City-Level Data?

Following up on our previous discussion about IP lookup tools, I'm still grappling with a persistent and critical issue: achieving reliable city-level data. While my application can accurately determine country and region for most IPs, the city field remains frustratingly elusive or highly inconsistent, even for well-known and active IP addresses.

This problem significantly impacts our user segmentation, analytics, and geo-targeting capabilities. Without granular city data, our ability to deliver localized content or analyze user behavior at a micro-level is severely hampered.

Here's what I've tried so far in an attempt to improve the geocoding accuracy:

• Provider Diversification: I've experimented with multiple IP geolocation providers, including MaxMind GeoLite2, IPinfo, and AbstractAPI. While each offers varying degrees of geocoding accuracy, no single provider consistently delivers city data for all relevant IPs. Switching between them often just shifts which IPs are problematic.
• Database Updates: For local databases like MaxMind GeoLite2, I've ensured they are updated daily or at least weekly. This rules out stale data as the primary cause for missing city information.
• Network & Proxy Analysis: I've investigated whether corporate VPNs, cloudflare proxy usage, or other anonymizing services might be masking the true user location at the city level. However, the issue persists even with what appear to be direct consumer IPs, making this a less likely universal explanation.
• Fallback Logic: I've implemented a tiered fallback system where if city data is missing, we default to region, and then country. While functional, this isn't ideal for our use case which demands city-level precision.
• Raw IP Data Inspection: I've manually checked specific problematic IPs against multiple public lookup services (e.g., ip-api.com, whatismyipaddress.com). This often confirms the inconsistency, with some services providing a city, others providing none, or even an incorrect one.

To illustrate the problem, here's an example of an actual (dummy) JSON response I frequently encounter, where the city data is missing:

{  "ip": "192.0.2.1",  "country": "US",  "region": "California",  "city": null, // Expected "San Francisco" but got null  "latitude": 37.77,  "longitude": -122.41}

In this scenario, I'd expect "San Francisco" or at least a nearby major city, given the IP's context, but I get null. This happens for a significant percentage of my lookups.

I'm reaching out to the community for insights. Specifically:

• Are there specific strategies or combinations of providers that significantly improve geocoding accuracy for city-level data? Perhaps a multi-provider aggregation strategy that I haven't fully optimized?
• How do others handle the trade-off between speed and data precision when dealing with real-time IP lookups, especially when needing city-level detail?
• Are there known issues with certain ISPs or entire IP blocks where city data is notoriously difficult to obtain, and if so, how do you mitigate this?

Has anyone else faced such persistent challenges with city-level geocoding accuracy and found a robust, scalable solution?